Supporting student success with machine learning and visual analytics

Post secondary institutions have a wealth of student data at their disposal. This data has recently been used to explore a problem that has been prevalent in the education domain for decades. Student retention is a complex issue that researchers are attempting to address using machine learning. This thesis describes our attempt to use academic data from Ontario Tech University to predict the likelihood of a student withdrawing from the university after their upcoming semester. We used academic data collected between 2007 and 2011 to train a random forest model that predicts whether or not a student will dropout. Finally, we used the confidence level of the model’s prediction to represent a students “likelihood of success”, which is displayed on a beeswarm plot as part of an application intended for use by academic advisors.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Weagant, Riley

Advisor dc:contributor.advisor

• Collins, Christopher

• Visual analytics
• Machine learning
• Student retention
• Education
• Predictive analytics

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1110

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1110